Table of Contents

Libraries

In [1]:
import re
import regex
import pandas as pd
import numpy as np
import emoji
import plotly.express as px
from collections import Counter
import matplotlib.pyplot as plt
from os import path
from PIL import Image
import datetime
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
%matplotlib inline

Data Pre-processing

In [2]:
def startsWithDateAndTimeAndroid(s):
    pattern = '^([0-9]+)(\/)([0-9]+)(\/)([0-9]+), ([0-9]+):([0-9]+)[ ]?(AM|PM|am|pm)? -' 
    result = re.match(pattern, s)
    if result:
        return True
    return False

def startsWithDateAndTimeios(s):
    pattern = '^\[([0-9]+)([\/-])([0-9]+)([\/-])([0-9]+)[,]? ([0-9]+):([0-9][0-9]):([0-9][0-9])?[ ]?(AM|PM|am|pm)?\]' 
    result = re.match(pattern, s)
    if result:
        return True
    return False
In [3]:
def FindAuthor(s):
  s=s.split(":")
  if len(s)==2:
    return True
  else:
    return False
In [4]:
def getDataPointAndroid(line):   
    splitLine = line.split(' - ') 
    dateTime = splitLine[0]
    date, time = dateTime.split(', ') 
    message = ' '.join(splitLine[1:])
    if FindAuthor(message): 
        splitMessage = message.split(':') 
        author = splitMessage[0] 
        message = ' '.join(splitMessage[1:])
    else:
        author = None
    return date, time, author, message

def getDataPointios(line):
    splitLine = line.split('] ')
    dateTime = splitLine[0]
    if ',' in dateTime:
        date, time = dateTime.split(',')
    else:
        date, time = dateTime.split(' ')
    message = ' '.join(splitLine[1:])
    if FindAuthor(message):
        splitMessage = message.split(':')
        author = splitMessage[0]
        message = ' '.join(splitMessage[1:])
    else:
        author = None
    if time[5]==":":
        time = time[:5]+time[-3:]
    else:
        if 'AM' in time or 'PM' in time:
            time = time[:6]+time[-3:]
        else:
            time = time[:6]
    return date, time, author, message
In [5]:
def dateconv(date):
  year=''
  if '-' in date:
    year = date.split('-')[2]
    if len(year) == 4:
      return datetime.datetime.strptime(date, "[%d-%m-%Y").strftime("%Y-%m-%d")
    elif len(year) ==2:
      return datetime.datetime.strptime(date, "[%d-%m-%y").strftime("%Y-%m-%d")
  elif '/' in date:
    year = date.split('/')[2]
    if len(year) == 4:
      return datetime.datetime.strptime(date, "[%d/%m/%Y").strftime("%Y-%m-%d")
    if len(year) ==2:
      return datetime.datetime.strptime(date, "[%d/%m/%y").strftime("%Y-%m-%d")
In [6]:
def split_count(text):

    emoji_list = []
    data = regex.findall(r'\X', text)
    for word in data:
        if any(char in emoji.UNICODE_EMOJI for char in word):
            emoji_list.append(word)

    return emoji_list
In [7]:
parsedData = [] # List to keep track of data so it can be used by a Pandas dataframe
conversationPath = "WhatsApp Chat with Data Science.txt" 
with open(conversationPath, encoding="utf-8") as fp:
    device=''
    first=fp.readline()
    print(first)
    if '[' in first:
      device='ios'
    else:
      device="android"
    fp.readline() 
    messageBuffer = [] 
    date, time, author = None, None, None
    while True:
        line = fp.readline() 
        if not line: 
            break
        if device=="ios":
          line = line.strip()
          if startsWithDateAndTimeios(line):
            if len(messageBuffer) > 0:
              parsedData.append([date, time, author, ' '.join(messageBuffer)])
            messageBuffer.clear()
            date, time, author, message = getDataPointios(line)
            messageBuffer.append(message)
          else:
            line= (line.encode('ascii', 'ignore')).decode("utf-8")
            if startsWithDateAndTimeios(line):
              if len(messageBuffer) > 0:
                parsedData.append([date, time, author, ' '.join(messageBuffer)])
              messageBuffer.clear()
              date, time, author, message = getDataPointios(line)
              messageBuffer.append(message)
            else:
              messageBuffer.append(line)
        else:
          line = line.strip()
          if startsWithDateAndTimeAndroid(line):
            if len(messageBuffer) > 0:
              parsedData.append([date, time, author, ' '.join(messageBuffer)])
            messageBuffer.clear()
            date, time, author, message = getDataPointAndroid(line)
            messageBuffer.append(message)
          else:
            messageBuffer.append(line)

2018/11/05, 00:45 - +27 84 667 4237 created group "Data Science"
In [8]:
if device =='android':
        df = pd.DataFrame(parsedData, columns=['Date', 'Time', 'Author', 'Message'])
        df["Date"] = pd.to_datetime(df["Date"])
        df = df.dropna()
        df["emoji"] = df["Message"].apply(split_count)
        URLPATTERN = r'(https?://\S+)'
        df['urlcount'] = df.Message.apply(lambda x: re.findall(URLPATTERN, x)).str.len()
else:
        df = pd.DataFrame(parsedData, columns=['Date', 'Time', 'Author', 'Message']) # Initialising a pandas Dataframe.
        df = df.dropna()
        df["Date"] = df["Date"].apply(dateconv)
        df["Date"] = pd.to_datetime(df["Date"],format='%Y-%m-%d')
        df["emoji"] = df["Message"].apply(split_count)
        URLPATTERN = r'(https?://\S+)'
        df['urlcount'] = df.Message.apply(lambda x: re.findall(URLPATTERN, x)).str.len()

Data Analysis

In [9]:
df.head()
Out[9]:
Date Time Author Message emoji urlcount
1 2019-09-22 22:59 Jocelyn Hey guys, does anyone know if we have classes... [🙈] 0
2 2019-09-22 23:06 Jocelyn Anyone? [] 0
3 2019-09-22 23:06 Sylveester 😅🤣😂, what do you think??? [😅, 🤣, 😂] 0
4 2019-09-22 23:06 Kerrin Krrrk krrrrk [] 0
5 2019-09-22 23:07 Kerrin Waiting for this message [] 0
In [10]:
df.tail()
Out[10]:
Date Time Author Message emoji urlcount
3522 2020-12-03 08:23 Taryn Thank you [] 0
3523 2020-12-07 18:31 Mfundo Capstone results out [] 0
3524 2020-12-07 18:31 Mfundo Go check on ienablr [] 0
3525 2020-12-17 10:04 Ellie The final results. And congratulations to ev... [😊] 0
3526 2020-12-17 10:07 Katlego Kgosi Thanks broskie, see u in the other side🤣🤙🏼 E... [🤣, 🤙🏼] 0

Chat Participants

In [11]:
df.Author.unique()
Out[11]:
array(['Jocelyn', 'Sylveester', 'Kerrin', 'Taryn', 'Katlego', 'Ellie',
       'Thabo', 'Nomz', 'Vusi', 'Phenyo', 'Mfundo', 'Hanna Sebolai',
       'Katlego Kgosi', '+27 73 246 5689', 'Tshepang', 'Isaac Thatayaone',
       'Kyle', 'Hanna Sebolai 2'], dtype=object)
In [12]:
df = df.dropna()
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 3439 entries, 1 to 3526
Data columns (total 6 columns):
 #   Column    Non-Null Count  Dtype         
---  ------    --------------  -----         
 0   Date      3439 non-null   datetime64[ns]
 1   Time      3439 non-null   object        
 2   Author    3439 non-null   object        
 3   Message   3439 non-null   object        
 4   emoji     3439 non-null   object        
 5   urlcount  3439 non-null   int64         
dtypes: datetime64[ns](1), int64(1), object(4)
memory usage: 188.1+ KB

More fields

In [13]:
# Get Year from Date
df['Date'] = pd.to_datetime(df['Date'])
df['Year']=df['Date'].dt.year

# Get Month from Date
df['Month']=df['Date'].dt.month

# Get Day from Date
df['Day']=df['Date'].dt.day

# Get hour from Time
df['Hour'] = df['Time'].str[:2]

# Get Media shared in the Message
df['Media']=df['Message'].str.contains('<Media omitted>')
In [14]:
df.head()
Out[14]:
Date Time Author Message emoji urlcount Year Month Day Hour Media
1 2019-09-22 22:59 Jocelyn Hey guys, does anyone know if we have classes... [🙈] 0 2019 9 22 22 False
2 2019-09-22 23:06 Jocelyn Anyone? [] 0 2019 9 22 23 False
3 2019-09-22 23:06 Sylveester 😅🤣😂, what do you think??? [😅, 🤣, 😂] 0 2019 9 22 23 False
4 2019-09-22 23:06 Kerrin Krrrk krrrrk [] 0 2019 9 22 23 False
5 2019-09-22 23:07 Kerrin Waiting for this message [] 0 2019 9 22 23 False

Groupwise stats

In [15]:
total_messages = df.shape[0]
print(total_messages)

3439
In [16]:
media_messages = df[df['Message'] == ' <Media omitted>'].shape[0]
print(media_messages)

204
In [17]:
media_messages =df[(df['Message'] == ' <Media omitted>')|(df['Message'] == ' image omitted')|(df['Message'] == ' video omitted')|(df['Message'] == ' sticker omitted')].shape[0]
print(media_messages)

204
In [18]:
emojis = sum(df['emoji'].str.len())
print(emojis)

2668
In [19]:
URLPATTERN = r'(https?://\S+)'
df['urlcount'] = df.Message.apply(lambda x: re.findall(URLPATTERN, x)).str.len()
In [20]:
links = np.sum(df.urlcount)
In [21]:
links
Out[21]:
3
In [22]:
print("Group Wise Stats")
print("Messages:",total_messages)
print("Media:",media_messages)
print("Emojis:",emojis)
print("Links:",links)

Group Wise Stats
Messages: 3439
Media: 204
Emojis: 2668
Links: 3

Separating Media and Text Messages

In [23]:
link_messages= df[df['urlcount']>0]
deleted_messages=df[(df["Message"] == " You deleted this message")| (df["Message"] == " This message was deleted.")|(df["Message"] == " You deleted this message.")]
media_messages_df = df[(df['Message'] == ' <Media omitted>')|(df['Message'] == ' image omitted')|(df['Message'] == ' video omitted')|(df['Message'] == ' sticker omitted')]
messages_df = df.drop(media_messages_df.index)
messages_df = messages_df.drop(deleted_messages.index)
messages_df = messages_df.drop(link_messages.index)
messages_df['Letter_Count'] = messages_df['Message'].apply(lambda s : len(s))
messages_df['Word_Count'] = messages_df['Message'].apply(lambda s : len(s.split(' ')))
messages_df["MessageCount"]=1
messages_df["emojicount"]= df['emoji'].str.len()
In [24]:
messages_df.head()
Out[24]:
Date Time Author Message emoji urlcount Year Month Day Hour Media Letter_Count Word_Count MessageCount emojicount
1 2019-09-22 22:59 Jocelyn Hey guys, does anyone know if we have classes... [🙈] 0 2019 9 22 22 False 58 12 1 1
2 2019-09-22 23:06 Jocelyn Anyone? [] 0 2019 9 22 23 False 8 2 1 0
3 2019-09-22 23:06 Sylveester 😅🤣😂, what do you think??? [😅, 🤣, 😂] 0 2019 9 22 23 False 26 6 1 3
4 2019-09-22 23:06 Kerrin Krrrk krrrrk [] 0 2019 9 22 23 False 13 3 1 0
5 2019-09-22 23:07 Kerrin Waiting for this message [] 0 2019 9 22 23 False 25 5 1 0

Author Stats

In [25]:
l = messages_df.Author.unique()

for i in range(len(l)):
  # Filtering out messages of particular user
  req_df= messages_df[messages_df["Author"] == l[i]]
  # req_df will contain messages of only one particular user
  print(f'Stats of {l[i]} -')
  # shape will print number of rows which indirectly means the number of messages
  print('Messages Sent', req_df.shape[0])
  #Word_Count contains of total words in one message. Sum of all words/ Total Messages will yield words per message
  words_per_message = (np.sum(req_df['Word_Count']))/req_df.shape[0]
  print('Words per message', words_per_message)
  #media conists of media messages
  media = media_messages_df[media_messages_df['Author'] == l[i]].shape[0]
  print('Media Messages Sent', media)
  # emojis conists of total emojis
  emojis = sum(req_df['emoji'].str.len())
  print('Emojis Sent', emojis)
  #links consist of total links
  links = sum(link_messages[link_messages['Author'] == l[i]]["urlcount"])   
  print('Links Sent', links)   
  print()

Stats of Jocelyn -
Messages Sent 77
Words per message 19.01298701298701
Media Messages Sent 6
Emojis Sent 46
Links Sent 1

Stats of Sylveester -
Messages Sent 320
Words per message 12.94375
Media Messages Sent 44
Emojis Sent 225
Links Sent 0

Stats of Kerrin -
Messages Sent 283
Words per message 9.282685512367491
Media Messages Sent 19
Emojis Sent 346
Links Sent 0

Stats of Taryn -
Messages Sent 447
Words per message 11.574944071588368
Media Messages Sent 20
Emojis Sent 346
Links Sent 0

Stats of Katlego -
Messages Sent 45
Words per message 12.822222222222223
Media Messages Sent 0
Emojis Sent 21
Links Sent 0

Stats of Ellie -
Messages Sent 728
Words per message 8.557692307692308
Media Messages Sent 52
Emojis Sent 442
Links Sent 2

Stats of Thabo -
Messages Sent 40
Words per message 18.525
Media Messages Sent 7
Emojis Sent 7
Links Sent 0

Stats of Nomz -
Messages Sent 323
Words per message 10.662538699690403
Media Messages Sent 13
Emojis Sent 307
Links Sent 0

Stats of Vusi -
Messages Sent 1
Words per message 2.0
Media Messages Sent 0
Emojis Sent 7
Links Sent 0

Stats of Phenyo -
Messages Sent 61
Words per message 6.934426229508197
Media Messages Sent 0
Emojis Sent 61
Links Sent 0

Stats of Mfundo -
Messages Sent 675
Words per message 6.525925925925926
Media Messages Sent 20
Emojis Sent 734
Links Sent 0

Stats of Hanna Sebolai -
Messages Sent 21
Words per message 8.142857142857142
Media Messages Sent 1
Emojis Sent 0
Links Sent 0

Stats of Katlego Kgosi -
Messages Sent 184
Words per message 13.456521739130435
Media Messages Sent 21
Emojis Sent 122
Links Sent 0

Stats of +27 73 246 5689 -
Messages Sent 6
Words per message 29.0
Media Messages Sent 1
Emojis Sent 2
Links Sent 0

Stats of Tshepang -
Messages Sent 1
Words per message 5.0
Media Messages Sent 0
Emojis Sent 0
Links Sent 0

Stats of Isaac Thatayaone -
Messages Sent 6
Words per message 5.166666666666667
Media Messages Sent 0
Emojis Sent 0
Links Sent 0

Stats of Kyle -
Messages Sent 7
Words per message 5.142857142857143
Media Messages Sent 0
Emojis Sent 2
Links Sent 0

Stats of Hanna Sebolai 2 -
Messages Sent 5
Words per message 12.4
Media Messages Sent 0
Emojis Sent 0
Links Sent 0

Unique emojis

In [26]:
total_emojis_list = list(set([a for b in messages_df.emoji for a in b]))
total_emojis = len(total_emojis_list)
print(total_emojis)

183

Used emoji's

In [61]:
total_emojis_list = list([a for b in messages_df.emoji for a in b])
emoji_dict = dict(Counter(total_emojis_list))
emoji_dict = sorted(emoji_dict.items(), key=lambda x: x[1], reverse=True)
print(emoji_dict)

[('😂', 962), ('🤣', 256), ('😅', 171), ('👏', 76), ('👀', 72), ('👏🏼', 69), ('😒', 54), ('🙆\u200d♂️', 45), ('🙄', 43), ('😹', 40), ('🏃🏾\u200d♂️', 31), ('😑', 26), ('💔', 23), ('💀', 23), ('🚮', 22), ('🚶🏻\u200d♂️', 21), ('😐', 20), ('🤦🏻\u200d♂️', 20), ('✊🏾', 18), ('🤷🏻\u200d♂', 17), ('🌚', 17), ('🥳', 17), ('🤷🏽\u200d♀️', 17), ('😄', 16), ('🙈', 14), ('🙏🏼', 14), ('🙆🏽', 14), ('😁', 14), ('😏', 13), ('🚶🏽\u200d♂️', 13), ('😱', 13), ('😭', 13), ('👏🏽', 11), ('🏃🏾', 11), ('🤔', 11), ('🙆🏽\u200d♂️', 11), ('⚰️', 11), ('😳', 10), ('😫', 10), ('🥴', 10), ('🎊', 9), ('🙆🏽\u200d♀️', 9), ('😀', 9), ('🦦', 9), ('💯', 7), ('🤷🏾\u200d♂️', 7), ('🤯', 7), ('🤷🏽\u200d♂️', 7), ('😢', 7), ('🤦🏽\u200d♀️', 7), ('🥺', 7), ('🎉', 6), ('👍🏽', 6), ('😃', 6), ('🚶🏻', 6), ('🤷🏻\u200d♂️', 6), ('🤛', 6), ('😩', 6), ('😨', 6), ('🙌🏽', 6), ('🎈', 5), ('😆', 5), ('🚶🏾\u200d♂️', 5), ('✊🏽', 5), ('🔥', 5), ('🤷🏽\u200d♀', 5), ('🥱', 5), ('👍🏼', 5), ('👏🏾', 5), ('😪', 5), ('👏🏻', 5), ('🤧', 4), ('😉', 4), ('😌', 4), ('🙋🏻\u200d♀️', 4), ('🤫', 4), ('😰', 4), ('💃🏽', 4), ('🙃', 4), ('👍', 4), ('🙆🏻\u200d♀️', 4), ('✊', 4), ('🌺', 3), ('💃🏼', 3), ('😖', 3), ('👍🏾', 3), ('🤨', 3), ('💁🏻\u200d♂', 3), ('🙆🏻\u200d♂', 3), ('🤦🏽\u200d♀', 3), ('✊🏻', 3), ('🤦🏻\u200d♂', 3), ('🙆\u200d♂', 3), ('🧎🏻', 3), ('😥', 3), ('😕', 3), ('😔', 3), ('🤦🏻\u200d♀️', 3), ('💁🏻\u200d♀️', 3), ('😬', 3), ('✊🏼', 3), ('😯', 3), ('🤦🏻\u200d♀', 2), ('👆🏽', 2), ('🙋🏿\u200d♀️', 2), ('🔪', 2), ('🤦🏽\u200d♂️', 2), ('🤥', 2), ('😣', 2), ('🤭', 2), ('🤷🏻\u200d♀️', 2), ('🙂', 2), ('🗣️', 2), ('🤦🏼\u200d♂️', 2), ('🧐', 2), ('🙏🏾', 2), ('🤟🏼', 2), ('🤟🏾', 2), ('👌', 2), ('😦', 2), ('🙋🏽\u200d♀️', 2), ('☝🏼', 2), ('🥵', 1), ('⚰', 1), ('🙇🏽\u200d♀', 1), ('🚶🏾', 1), ('🙏', 1), ('🙇', 1), ('😿', 1), ('😶', 1), ('🙋🏻', 1), ('🙋🏽', 1), ('🤞🏼', 1), ('🤦🏾\u200d♂', 1), ('🤓', 1), ('😇', 1), ('🤩', 1), ('😜', 1), ('😲', 1), ('🛌🏻', 1), ('🚫', 1), ('🥶', 1), ('😷', 1), ('❌', 1), ('🏃\u200d♀️', 1), ('👩🏻\u200d🦯', 1), ('😞', 1), ('🗣', 1), ('🍌', 1), ('👊🏾', 1), ('🏃🏽\u200d♀️', 1), ('😎', 1), ('🌬️', 1), ('🤾🏾\u200d♂️', 1), ('🙌', 1), ('😓', 1), ('🙅🏻\u200d♀️', 1), ('🙇🏾', 1), ('😟', 1), ('🏃🏻\u200d♀️', 1), ('💪🏽', 1), ('🤦\u200d♂️', 1), ('💃🏻', 1), ('🙆🏻\u200d♂️', 1), ('👉🏽', 1), ('👈🏽', 1), ('☠️', 1), ('✋🏽', 1), ('🙋\u200d♀️', 1), ('✌🏽', 1), ('☝🏽', 1), ('🙆🏼\u200d♀️', 1), ('👌🏾', 1), ('🙌🏼', 1), ('🤛🏽', 1), ('😴', 1), ('❤️', 1), ('✅', 1), ('🐑', 1), ('🤷\u200d♂️', 1), ('🖐️', 1), ('😊', 1), ('🤙🏼', 1)]

Top 10 emoji's

In [62]:
emoji_df = pd.DataFrame(emoji_dict, columns=['emoji', 'count'])
emoji_df.head(10)
Out[62]:
emoji count
0 😂 962
1 🤣 256
2 😅 171
3 👏 76
4 👀 72
5 👏🏼 69
6 😒 54
7 🙆‍♂️ 45
8 🙄 43
9 😹 40

Emoji Distribution

In [29]:
import plotly.express as px
fig = px.pie(emoji_df, values='count', names='emoji', title= "Emoji's Used")
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()

Person wise emoji Distribution

In [30]:
l = messages_df.Author.unique()
for i in range(len(l)):
  dummy_df = messages_df[messages_df['Author'] == l[i]]
  total_emojis_list = list([a for b in dummy_df.emoji for a in b])
  emoji_dict = dict(Counter(total_emojis_list))
  emoji_dict = sorted(emoji_dict.items(), key=lambda x: x[1], reverse=True)
  print('Emoji Distribution for', l[i])
  author_emoji_df = pd.DataFrame(emoji_dict, columns=['emoji', 'count'])
  fig = px.pie(author_emoji_df, values='count', names='emoji')
  fig.update_traces(textposition='inside', textinfo='percent+label')
  fig.show()

Emoji Distribution for Jocelyn

Emoji Distribution for Sylveester

Emoji Distribution for Kerrin

Emoji Distribution for Taryn

Emoji Distribution for Katlego

Emoji Distribution for Ellie

Emoji Distribution for Thabo

Emoji Distribution for Nomz

Emoji Distribution for Vusi

Emoji Distribution for Phenyo

Emoji Distribution for Mfundo

Emoji Distribution for Hanna Sebolai

Emoji Distribution for Katlego Kgosi

Emoji Distribution for +27 73 246 5689

Emoji Distribution for Tshepang

Emoji Distribution for Isaac Thatayaone

Emoji Distribution for Kyle

Emoji Distribution for Hanna Sebolai 2

Engagement by weekday

In [31]:
def f(i):
  l = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
  return l[i];
day_df=pd.DataFrame(messages_df["Message"])
day_df['day_of_date'] = messages_df['Date'].dt.weekday
day_df['day_of_date'] = day_df["day_of_date"].apply(f)
day_df["messagecount"] = 1
day = day_df.groupby("day_of_date").sum()
day.reset_index(inplace=True)
In [32]:
fig = px.line_polar(day, r='messagecount', theta='day_of_date', line_close=True)
fig.update_traces(fill='toself')
fig.update_layout(
  polar=dict(
    radialaxis=dict(
      visible=True,
    )),
  showlegend=False
)
fig.show()

Number of messages sent over time

In [58]:
date_df = messages_df.groupby("Date").sum()
date_df.reset_index(inplace=True)
fig = px.line(date_df, x="Date", y="MessageCount")
fig.update_xaxes(nticks=20)
# Edit the layout
#fig.update_layout(title='Name',
 #                  xaxis_title='Name',
  #                 yaxis_title='Name')
fig.show()
#fig.write_image("messages.png")
In [34]:
date_df["rolling"] = date_df["MessageCount"].rolling(30).mean()
fig = px.line(date_df, x="Date", y="rolling")
fig.update_xaxes(nticks=20)
fig.show()

Most Active Chatter

In [45]:
auth = messages_df.groupby("Author").sum()
auth.reset_index(inplace=True)
fig = px.bar(auth, y="Author", x="MessageCount", color='Author', orientation="h",
             color_discrete_sequence=["black", "brown", "rosybrown", "darkorange", "darkgoldenrod"],
             title="Number of Messages Sent Per User"
            )

fig.show()

Number of words used

In [38]:
text = " ".join(review for review in messages_df.Message)
print ("There are {} words in all the messages.".format(len(text)))

There are 158319 words in all the messages.

Most Common words (Group)

In [39]:
text = " ".join(review for review in messages_df.Message)
stopwords = set(STOPWORDS)
stopwords.update(["ra", "ga", "na", "ani", "em", "ki", "ah","ha","la","eh","ne","le","ni","lo","Ma","Haa","ni", "u",
                 "us", "guy", "guys", "will", "now", "know"])
  # Generate a word cloud image
wordcloud = WordCloud(stopwords=stopwords, colormap="Oranges").generate(text)
  # Display the generated image:
  # the matplotlib way:
  
plt.figure( figsize=(10,5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()
#plt.savefig("cloud.jpg")

Most common words used by participants

In [40]:
l = messages_df.Author.unique()
for i in range(len(l)):
    try:
        dummy_df = messages_df[messages_df['Author'] == l[i]]
        text = " ".join(review for review in dummy_df.Message)
        stopwords = set(STOPWORDS)
        stopwords.update(["ra", "ga", "na", "ani", "em", "ki", "ah","ha","la","eh","ne","le","ni","lo","Ma","Haa","ni", "u",
                     "us", "guy", "guys", "will", "now", "know"])
        # Generate a word cloud image
        print('Author name',l[i])
        wordcloud = WordCloud(stopwords=stopwords, colormap="Oranges").generate(text)
        # Display the generated image:
        # the matplotlib way:

        plt.figure( figsize=(10,5))
        plt.imshow(wordcloud, interpolation='bilinear')
        plt.axis("off")
        plt.show()
    except Exception:
        pass

Author name Jocelyn

Author name Sylveester

Author name Kerrin

Author name Taryn

Author name Katlego

Author name Ellie

Author name Thabo

Author name Nomz

Author name Vusi
Author name Phenyo

Author name Mfundo

Author name Hanna Sebolai

Author name Katlego Kgosi

Author name +27 73 246 5689

Author name Tshepang

Author name Isaac Thatayaone

Author name Kyle

Author name Hanna Sebolai 2

Time of the day with most messages

In [44]:
timeChat = messages_df['Time'].value_counts().nlargest(15).rename_axis("Time").reset_index(name='Count')
fig = px.bar(timeChat, y="Time", x="Count", color='Time', orientation="h",
             color_discrete_sequence=["black", "brown", "rosybrown", "darkorange", "darkgoldenrod"],
             title="Most Active Time of the Day"
            )

fig.show()

Most Active hour of the day

In [59]:
activeHour = messages_df.groupby(['Hour'])['Hour'].count().nlargest(15).rename_axis("Hour").reset_index(name='Count')
fig = px.bar(activeHour, y="Hour", x="Count", color='Hour', orientation="h",
             color_discrete_sequence=["black", "brown", "rosybrown", "darkorange", "darkgoldenrod"],
             title="Most Active Hour of the Day"
            )

fig.show()